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Record W2002444644 · doi:10.1038/oby.2010.72

BMI Independently Predicts Younger Age at Hip and Knee Replacement

2010· article· en· W2002444644 on OpenAlexaffabout
Rajiv Gandhi, David Wasserstein, Fahad Razak, J. Roderick Davey, Nizar N. Mahomed

Bibliographic record

VenueObesity · 2010
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineWOMACOsteoarthritisBody mass indexKnee replacementHip replacementObesityComorbidityHip surgeryJoint replacementInternal medicineArthroplastyPhysical therapySurgery

Abstract

fetched live from OpenAlex

Obesity has been identified as a risk factor for the development of hip and knee osteoarthritis (OA) and may play a role in exacerbating existing disease. Therefore, we hypothesized that obese patients would present for hip and knee replacement surgery at a younger age than nonobese patients. From our registry, we performed a cross-sectional study of 841 hip and 804 knee replacement patients. Patients were categorized by BMI ≤ 25 kg/m(2), 25.1-29.9 kg/m(2), 30-34.9 kg/m(2), and ≥ 35 kg/m(2). Linear regression modeling was used to examine the relationship between BMI and age at surgery. Hip and knee replacement patients' mean age at surgery was 7.1 and 7.9 years younger, respectively, if their BMI was ≥ 35 kg/m(2) when compared to patients with a BMI ≤ 25 kg/m(2) (P = 0.002). BMI was a significant independent (of gender, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, surgeon, and comorbidity) predictor of age at knee replacement (P < 0.05). WOMAC scores were significantly worse preoperatively in patients with a BMI ≥ 35 kg/m(2) compared to those with a BMI ≤ 25 kg/m(2) (P < 0.05). Our study indicates that obese patients, especially those with a BMI ≥ 35 kg/m(2), presented for and underwent joint replacement surgery at a younger age as compared to nonobese patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.241
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations88
Published2010
Admission routes2
Has abstractyes

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